Jude Shavlik

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Richard MaclinProfessor of Computer Science, University of Minnesota DuluthVerified email at d.umn.edu
Sriraam NatarajanUniversity of Texas at DallasVerified email at utdallas.edu
Kristian KerstingProfessor of Artificial Intelligence and Machine Learning, hessian.ai, TU Darmstadt, GermanyVerified email at cs.tu-darmstadt.de
Feng NiuUniversity of Wisconsin-MadisonVerified email at cs.wisc.edu
Raymond MooneyProfessor of Computer Science, University of Texas at AustinVerified email at cs.utexas.edu
Thomas DietterichDistinguished Professor (Emeritus), Computer Science, Oregon State UniversityVerified email at cs.orst.edu
Ce ZhangETH ZurichVerified email at inf.ethz.ch
Gautam KunapuliVerisk AI R&DVerified email at verisk.com
Tina Eliassi-RadProfessor of Computer Science, Northeastern UniversityVerified email at eliassi.org
Vítor Santos CostaProfessor of Computer Science, Universidade do PortoVerified email at dcc.fc.up.pt
Olvi MangasarianProfessor Computer Sciences, University of Wisconsin MadisonVerified email at cs.wisc.edu
Houssam NassifAmazonVerified email at amazon.com
Raghu RamakrishnanMicrosoftVerified email at microsoft.com
Ines DutraProfessor of Computer Science, University of PortoVerified email at dcc.fc.up.pt
Jeremy GoecksAssociate Professor of Computational Biology, Oregon Health and Science UniversityVerified email at ohsu.edu
Mark GoadrichProfessor of Computer Science, Hendrix CollegeVerified email at hendrix.edu
Jesse DavisProfessor, Department of Computer Science, KU LeuvenVerified email at cs.kuleuven.be
Peter StoneProfessor of Computer Science, The University of Texas at AustinVerified email at cs.utexas.edu
Kristin P. BennettProfessor of Mathematical Sciences, Renssealer Polytechnic InstituteVerified email at rpi.edu
Saket JoshiApplied Scientist, AmazonVerified email at amazon.com

Jude Shavlik

Professor of Computer Science and of Biostatistics & Medical Informatics, University of Wisconsin

Verified email at cs.wisc.edu - Homepage

artificial intelligence machine learning computational biology bioinformatics medical informatics


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Introduction to statistical relational learning D Koller, N Friedman, S Džeroski, C Sutton, A McCallum, A Pfeffer, ... MIT press, 2007	1753	2007
Extracting refined rules from knowledge-based neural networks GG Towell, JW Shavlik Machine learning 13 (1), 71-101, 1993	1108	1993
Transfer learning L Torrey, J Shavlik Handbook of research on machine learning applications and trends: algorithms …, 2010	998	2010
Knowledge-based artificial neural networks GG Towell, JW Shavlik Artificial intelligence 70 (1-2), 119-165, 1994	962	1994
Extracting tree-structured representations of trained networks MW Craven, JW Shavlik Advances in neural information processing systems, 24-30, 1996	709	1996
An empirical comparison of pattern recognition, neural nets, and machine learning classification methods. SM Weiss, I Kapouleas IJCAI 89, 781-787, 1989	618	1989
Refinement of approximate domain theories by knowledge-based neural networks GG Towell, JW Shavlik, MO Noordewier Proceedings of the eighth National conference on Artificial intelligence 861866, 1990	611	1990
A 14nm logic technology featuring 2^nd-generation FinFET, air-gapped interconnects, self-aligned double patterning and a 0.0588 µm²SRAM cell size S Natarajan, M Agostinelli, S Akbar, M Bost, A Bowonder, V Chikarmane, ... 2014 IEEE International Electron Devices Meeting, 3.7. 1-3.7. 3, 2014	541	2014
Symbolic and neural learning algorithms: An experimental comparison JW Shavlik, RJ Mooney, GG Towell Machine learning 6 (2), 111-143, 1991	468	1991
Readings in machine learning JW Shavlik, T Dietterich, TG Dietterich Morgan Kaufmann, 1990	453	1990
Actively searching for an effective neural network ensemble DW Opitz, JW Shavlik Connection Science 8 (3-4), 337-354, 1996	427	1996
Generating accurate and diverse members of a neural-network ensemble DW Opitz, JW Shavlik Advances in neural information processing systems, 535-541, 1996	396	1996
Using sampling and queries to extract rules from trained neural networks, in machine learning MW Craven, JW Shavlik Proceedings of the Eleventh International Conference on Machine Learning, 73-80, 1994	388*	1994
Using sampling and queries to extract rules from trained neural networks MW Craven, JW Shavlik Proceedings of the 11th International Conference on Machine Learning, 37-45, 1994	388	1994
Using neural networks for data mining MW Craven, JW Shavlik Future generation computer systems 13 (2-3), 211-229, 1997	382	1997
Tuffy: Scaling up statistical inference in markov logic networks using an rdbms F Niu, C Ré, AH Doan, J Shavlik arXiv preprint arXiv:1104.3216, 2011	322	2011
Extracting comprehensible models from trained neural networks MW Craven University of Wisconsin-Madison Department of Computer Sciences, 1996	302	1996
Learning users' interests by unobtrusively observing their normal behavior J Goecks, J Shavlik Proceedings of the 5th international conference on Intelligent user …, 2000	284	2000
1 markov logic: A unifying framework for statistical relational learning P Domingos, M Richardson Statistical Relational Learning, 339, 2007	274	2007
Creating advice-taking reinforcement learners R Maclin, JW Shavlik Machine Learning 22 (1), 251-281, 1996	249	1996

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